Improving Flood Monitoring Through Advanced Modeling of Sentinel-1 Multi-Temporal Stacks

Multi-temporal remotely sensed data are a precious source of information for high spatial and temporal resolution flood mapping. We present a methodology for flood mapping through processing of long time series of Sentinel-l SAR data, as well as ancillary information. A Bayesian framework is adopted to derive probabilistic maps of the presence of flood waters, through modeling of backscatter time series, based on the as-sumption that floods represent impulsive temporal anomalies. We illustrate some results on a time series of Sentinel-l data acquired from 2015 to 2021 over a test area on the Basento river watershed, Basilicata Region, in Southern Italy, recurrently subject to floods.